pandas: multiple conditions while indexing data frame - unexpected behavior

As you can see, the AND operator drops every row in which at least one value equals -1. On the other hand, the OR operator requires both values to be equal to -1 to drop them.

That's right. Remember that you're writing the condition in terms of what you want to keep, not in terms of what you want to drop. For df1:

df1 = df[(df.a != -1) & (df.b != -1)]

You're saying "keep the rows in which df.a isn't -1 and df.b isn't -1", which is the same as dropping every row in which at least one value is -1.

For df2:

df2 = df[(df.a != -1) | (df.b != -1)]

You're saying "keep the rows in which either df.a or df.b is not -1", which is the same as dropping rows where both values are -1.

PS: chained access like df['a'][1] = -1 can get you into trouble. It's better to get into the habit of using .loc and .iloc.

You can also use query(), i.e.:

df_filtered = df.query('a == 4 & b != 2')

A little mathematical logic theory here:

"NOT a AND NOT b" is the same as "NOT (a OR b)", so:

"a NOT -1 AND b NOT -1" is equivalent of "NOT (a is -1 OR b is -1)", which is opposite (Complement) of "(a is -1 OR b is -1)".

So if you want exact opposite result, df1 and df2 should be as below:

df1 = df[(df.a != -1) & (df.b != -1)]df2 = df[(df.a == -1) | (df.b == -1)]